OpenRaiser/Intern-Atlas huggingface dataset:
On the state of reporting in crowdsourcing experiments and a checklist to aid current practices
Venue: arXiv cs.HC 2021
Year: 2021

Crowdsourcing is being increasingly adopted as a platform to run studies with human subjects. Running a crowdsourcing experiment involves several choices and strategies to successfully port an experimental design into an otherwise uncontrolled research environment, e.g., sampling crowd workers, mapping experimental conditions to micro-tasks, or ensure quality contributions. While several guidelines inform researchers in these choices, guidance of how and what to report from crowdsourcing experiments has been largely overlooked. If under-reported, implementation choices constitute variability sources that can affect the experiment's reproducibility and prevent a fair assessment of research outcomes. In this paper, we examine the current state of reporting of crowdsourcing experiments and offer guidance to address associated reporting issues. We start by identifying sensible implementation choices, relying on existing literature and interviews with experts, to then extensively analyze the reporting of 171 crowdsourcing experiments. Informed by this process, we propose a checklist for reporting crowdsourcing experiments.
United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks
Venue: arXiv cs.CV 2021
Year: 2021

Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification. Though ways of best harvesting learning improvements from hierarchies in classification are far from being solved, there is a need to target these problems in other vision tasks such as object detection. As progress on the classification side is often dependent on hierarchical cross-entropy losses, novel detection architectures using sigmoid as an output function instead of softmax cannot easily apply these advances, requiring novel methods in detection. In this work we establish a theoretical framework based on probability and set theory for extracting parent predictions and a hierarchical loss that can be used across tasks, showing results across classification and detection benchmarks and opening up the possibility of hierarchical learning for sigmoid-based detection architectures.
Novel Direct Algorithm for Computing Simultaneous All-Levels Reliability of Multi-state Flow Networks
Venue: arXiv cs.DM 2021
Year: 2021

All kind of networks, e.g., Internet of Things, social networks, wireless sensor networks, transportation networks, 4g/5G, etc., are around us to benefit and help our daily life. The multistate flow network (MFN) is always used to model network structures and applications. The level d reliability, Rd, of the MFN is the success probability of sending at least d units of integer flow from the source node to the sink node. The reliability Rd is a popular index for designing, managing, controlling, and evaluating MFNs. The traditional indirect algorithms must have all d-MPs (special connected vectors) or d-MCs (special disconnected vectors) first, then use Inclusion-Exclusion Technique (IET) or Sum-of-disjoint Product (SDP) in terms of found d-MPs or d-MCs to calculate Rd. The above four procedures are all NP-Hard and #P-Hard and cannot calculate Rd for all d at the same time A novel algorithm based on the binary-addition-tree algorithm (BAT) is proposed to calculate the Rd directly for all d at the same time without using any of the above four procedures. The time complexity and demonstration of the proposed algorithm are analyzed, and examples are provided. An experiment is also conducted to compare the proposed algorithm and existing algorithms based on d-MPs, d-MCs, IET, and/or SDP to validate the proposed algorithm.
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Non-autoregressive neural machine translation (NAT) usually employs sequence-level knowledge distillation using autoregressive neural machine translation (AT) as its teacher model. However, a NAT model often outputs shorter sentences than an AT model. In this work, we propose sequence-level knowledge distillation (SKD) using perturbed length-aware positional encoding and apply it to a student model, the Levenshtein Transformer. Our method outperformed a standard Levenshtein Transformer by 2.5 points in bilingual evaluation understudy (BLEU) at maximum in a WMT14 German to English translation. The NAT model output longer sentences than the baseline NAT models.
Maximize the Foot Clearance for a Hopping Robotic Leg Considering Motor Saturation
Venue: arXiv cs.RO 2021
Year: 2021

A hopping leg, no matter in legged animals or humans, usually behaves like a spring during the periodic hopping. Hopping like a spring is efficient and without the requirement of complicated control algorithms. Position and force control are two main methods to realize such a spring-like behaviour. The position control usually consumes the torque resources to ensure the position accuracy and compensate the tracking errors. In comparison, the force control strategy is able to maintain a high elasticity. Currently, the position and force control both leads to the discount of motor saturation ratio as well as the bandwidth of the control system, and thus attenuates the performance of the actuator. To augment the performance, this letter proposes a motor saturation strategy based on the force control to maximize the output torque of the actuator and realize the continuous hopping motion with natural dynamics. The proposed strategy is able to maximize the saturation ratio of motor and thus maximize the foot clearance of the single leg. The dynamics of the two-mass model is utilized to increase the force bandwidth and the performance of the actuator. A single leg with two degrees of freedom is designed as the experiment platform. The actuator consists of a powerful electric motor, a harmonic gear and encoder. The effectiveness of this method is verified through simulations and experiments using a robotic leg actuated by powerful high reduction ratio actuators.
Cascaded Residual Density Network for Crowd Counting
Venue: arXiv cs.CV 2021
Year: 2021

Crowd counting is a challenging task due to the issues such as scale variation and perspective variation in real crowd scenes. In this paper, we propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine approach to generate the high-quality density map for crowd counting more accurately. (1) We estimate the residual density maps by multi-scale pyramidal features through cascaded residual density modules. It can improve the quality of density map layer by layer effectively. (2) A novel additional local count loss is presented to refine the accuracy of crowd counting, which reduces the errors of pixel-wise Euclidean loss by restricting the number of people in the local crowd areas. Experiments on two public benchmark datasets show that the proposed method achieves effective improvement compared with the state-of-the-art methods.
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection
Venue: arXiv cs.CV 2021
Year: 2021

Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable. Despite that many approaches have been developed to resolve this problem, few of them can capture the normal spatio-temporal patterns effectively and efficiently. Moreover, existing works seldom explicitly consider the local consistency at frame level and global coherence of temporal dynamics in video sequences. To this end, we propose Convolutional Transformer based Dual Discriminator Generative Adversarial Networks (CT-D2GAN) to perform unsupervised video anomaly detection. Specifically, we first present a convolutional transformer to perform future frame prediction. It contains three key components, i.e., a convolutional encoder to capture the spatial information of the input video clips, a temporal self-attention module to encode the temporal dynamics, and a convolutional decoder to integrate spatio-temporal features and predict the future frame. Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction. Finally, the prediction error is used to identify abnormal video frames. Thoroughly empirical studies on three public video anomaly detection datasets, i.e., UCSD Ped2, CUHK Avenue, and Shanghai Tech Campus, demonstrate the effectiveness of the proposed adversarial spatio-temporal modeling framework.
Presenting the cyclotactor project
Venue: arXiv cs.HC 2021
Year: 2021

The cyclotactor is a novel platform for finger-based tactile interaction research. The operating principle is to track vertical fingerpad position above a freely approachable surface aperture, while directly projecting a force on the same fingerpad. The projected force can be specified in Newtons, with high temporal resolution. In combination with a relatively low overall latency between tactile input and output, this is used to work towards the ideal of instant programmable haptic feedback. This enables support for output across the continuum between static force levels and vibrotactile feedback, targeting both the kinesthetic and cutaneous senses of touch. The current state of the technology is described, and an overview of the research goals of the cyclotactor project is given.
ReCo1: A Fault resilient technique of Correlation Sensitive Stochastic Designs
Venue: arXiv cs.ET 2021
Year: 2021

In stochastic circuits, major sources of error are correlation errors, soft errors and random fluctuation errors that affect the accuracy and reliability of the circuit. The soft error has the effect of changing the correlation status and in turn changes the probability of numbers leading to the erroneous output. This has serious impact on security and medical systems where highly accurate systems are required. We tackle this problem by introducing the fault-tolerant technique of correlation-sensitive stochastic logic circuits. We develop a framework of Remodelling Correlation(ReCo) for Stochastic Logic Elements; AND, XOR and OR for reliable operation. We present two variants of ReCo models in combinational circuits with contradictory requirements by stating two interesting case studies. The proposed technique selects logic elements and places correction blocks based on a priority-based rule that helps to converge to the desired MSE quickly requiring less hardware area. It is shown that this technique does not alter the reliability of the overall circuit. To demonstrate the practical effectiveness of the proposed framework, contrast stretch operation on a standard image in a noisy environment is studied. A high structural similarity index measure of 92.80 is observed for the output image with the proposed approach compared to the image (with error) 66.43.
Revisiting Swapping in User-space with Lightweight Threading
Venue: arXiv cs.OS 2021
Year: 2021

Memory-intensive applications, such as in-memory databases, caching systems and key-value stores, are increasingly demanding larger main memory to fit their working sets. Conventional swapping can enlarge the memory capacity by paging out inactive pages to disks. However, the heavy I/O stack makes the traditional kernel-based swapping suffers from several critical performance issues. In this paper, we redesign the swapping system and propose LightSwap, an high-performance user-space swapping scheme that supports paging with both local SSDs and remote memories. First, to avoids kernel-involving, a novel page fault handling mechanism is proposed to handle page faults in user-space and further eliminates the heavy I/O stack with the help of user-space I/O drivers. Second, we co-design Lightswap with light weight thread (LWT) to improve system throughput and make it be transparent to user applications. Finally, we propose a try-catch framework in Lightswap to deal with paging errors which are exacerbated by the scaling in process technology. We implement Lightswap in our production-level system and evaluate it with YCSB workloads running on memcached. Results show that Ligthswap reduces the page faults handling latency by 3--5 times, and improves the throughput of memcached by more than 40% compared with the stat-of-art swapping systems.
Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM
Venue: arXiv cs.LG 2021
Year: 2021

Groundwater is the largest storage of freshwater resources, which serves as the major inventory for most of the human consumption through agriculture, industrial, and domestic water supply. In the fields of hydrological, some researchers applied a neural network to forecast rainfall intensity in space-time and introduced the advantages of neural networks compared to numerical models. Then, many researches have been conducted applying data-driven models. Some of them extended an Artificial Neural Networks (ANNs) model to forecast groundwater level in semi-confined glacial sand and gravel aquifer under variable state, pumping extraction and climate conditions with significant accuracy. In this paper, a multi-layer perceptron is applied to simulate groundwater level. The adaptive moment estimation optimization algorithm is also used to this matter. The root mean squared error, mean absolute error, mean squared error and the coefficient of determination ( ) are used to evaluate the accuracy of the simulated groundwater level. Total value of and RMSE are 0.9458 and 0.7313 respectively which are obtained from the model output. Results indicate that deep learning algorithms can demonstrate a high accuracy prediction. Although the optimization of parameters is insignificant in numbers, but due to the value of time in modelling setup, it is highly recommended to apply an optimization algorithm in modelling.
Developers perception on the severity of test smells: an empirical study
Venue: arXiv cs.SE 2021
Year: 2021

Unit testing is an essential component of the software development life-cycle. A developer could easily and quickly catch and fix software faults introduced in the source code by creating and running unit tests. Despite their importance, unit tests are subject to bad design or implementation decisions, the so-called test smells. These might decrease software systems quality from various aspects, making it harder to understand, more complex to maintain, and more prone to errors and bugs. Many studies discuss the likely effects of test smells on test code. However, there is a lack of studies that capture developers perceptions of such issues. This study empirically analyzes how developers perceive the severity of test smells in the test code they develop. Severity refers to the degree to how a test smell may negatively impact the test code. We selected six open-source software projects from GitHub and interviewed their developers to understand whether and how the test smells affected the test code. Although most of the interviewed developers considered the test smells as having a low severity to their code, they indicated that test smells might negatively impact the project, particularly in test code maintainability and evolution. Also, detecting and removing test smells from the test code may be positive for the project.
Lyapunov-based uncertainty-aware safe reinforcement learning
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning (RL) has shown a promising performance in learning optimal policies for a variety of sequential decision-making tasks. However, in many real-world RL problems, besides optimizing the main objectives, the agent is expected to satisfy a certain level of safety (e.g., avoiding collisions in autonomous driving). While RL problems are commonly formalized as Markov decision processes (MDPs), safety constraints are incorporated via constrained Markov decision processes (CMDPs). Although recent advances in safe RL have enabled learning safe policies in CMDPs, these safety requirements should be satisfied during both training and in the deployment process. Furthermore, it is shown that in memory-based and partially observable environments, these methods fail to maintain safety over unseen out-of-distribution observations. To address these limitations, we propose a Lyapunov-based uncertainty-aware safe RL model. The introduced model adopts a Lyapunov function that converts trajectory-based constraints to a set of local linear constraints. Furthermore, to ensure the safety of the agent in highly uncertain environments, an uncertainty quantification method is developed that enables identifying risk-averse actions through estimating the probability of constraint violations. Moreover, a Transformers model is integrated to provide the agent with memory to process long time horizons of information via the self-attention mechanism. The proposed model is evaluated in grid-world navigation tasks where safety is defined as avoiding static and dynamic obstacles in fully and partially observable environments. The results of these experiments show a significant improvement in the performance of the agent both in achieving optimality and satisfying safety constraints.
The Robustness of Graph k-shell Structure under Adversarial Attacks
Venue: arXiv cs.SI 2021
Year: 2021

The k-shell decomposition plays an important role in unveiling the structural properties of a network, i.e., it is widely adopted to find the densest part of a network across a broad range of scientific fields, including Internet, biological networks, social networks, etc. However, there arises concern about the robustness of the k-shell structure when networks suffer from adversarial attacks. Here, we introduce and formalize the problem of the k-shell attack and develop an efficient strategy to attack the k-shell structure by rewiring a small number of links. To the best of our knowledge, it is the first time to study the robustness of graph k-shell structure under adversarial attacks. In particular, we propose a Simulated Annealing (SA) based k-shell attack method and testify it on four real-world social networks. The extensive experiments validate that the k-shell structure of a network is robust under random perturbation, but it is quite vulnerable under adversarial attack, e.g., in Dolphin and Throne networks, more than 40% nodes change their k-shell values when only 10% links are changed based on our SA-based k-shell attack. Such results suggest that a single structural feature could also be significantly disturbed when only a small fraction of links are changed purposefully in a network. Therefore, it could be an interesting topic to improve the robustness of various network properties against adversarial attack in the future.
Significance of Speaker Embeddings and Temporal Context for Depression Detection
Venue: arXiv cs.CY 2021
Year: 2021

Depression detection from speech has attracted a lot of attention in recent years. However, the significance of speaker-specific information in depression detection has not yet been explored. In this work, we analyze the significance of speaker embeddings for the task of depression detection from speech. Experimental results show that the speaker embeddings provide important cues to achieve state-of-the-art performance in depression detection. We also show that combining conventional OpenSMILE and COVAREP features, which carry complementary information, with speaker embeddings further improves the depression detection performance. The significance of temporal context in the training of deep learning models for depression detection is also analyzed in this paper.
PAD: a graphical and numerical enhancement of structural coding to facilitate thematic analysis of a literature corpus
Venue: arXiv cs.DL 2021
Year: 2021

We suggest an enhancement to structural coding through the use of (a) causally bound codes, (b) basic constructs of graph theory and (c) statistics. As is the norm with structural coding, the codes are collected into categories. The categories are represented by nodes (graph theory). The causality is illustrated through links (graph theory) between the nodes and the entire set of linked nodes is collected into a single directed acyclic graph. The number of occurrences of the nodes and the links provide the input required to analyze relative frequency of occurrence, as well as opening a scope for further statistical analysis. While our raw data was a corpus of literature from a specific discipline, this enhancement is accessible to any qualitative analysis that recognizes causality in its structural codes.
Resisting Out-of-Distribution Data Problem in Perturbation of XAI
Venue: arXiv cs.AI 2021
Year: 2021

With the rapid development of eXplainable Artificial Intelligence (XAI), perturbation-based XAI algorithms have become quite popular due to their effectiveness and ease of implementation. The vast majority of perturbation-based XAI techniques face the challenge of Out-of-Distribution (OoD) data -- an artifact of randomly perturbed data becoming inconsistent with the original dataset. OoD data leads to the over-confidence problem in model predictions, making the existing XAI approaches unreliable. To our best knowledge, the OoD data problem in perturbation-based XAI algorithms has not been adequately addressed in the literature. In this work, we address this OoD data problem by designing an additional module quantifying the affinity between the perturbed data and the original dataset distribution, which is integrated into the process of aggregation. Our solution is shown to be compatible with the most popular perturbation-based XAI algorithms, such as RISE, OCCLUSION, and LIME. Experiments have confirmed that our methods demonstrate a significant improvement in general cases using both computational and cognitive metrics. Especially in the case of degradation, our proposed approach demonstrates outstanding performance comparing to baselines. Besides, our solution also resolves a fundamental problem with the faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears to be sensitive to the OoD issue.
Stochastic Geometric Iterative Method for Loop Subdivision Surface Fitting
Venue: arXiv cs.GR 2021
Year: 2021

In this paper, we propose a stochastic geometric iterative method to approximate the high-resolution 3D models by finite Loop subdivision surfaces. Given an input mesh as the fitting target, the initial control mesh is generated using the mesh simplification algorithm. Then, our method adjusts the control mesh iteratively to make its finite Loop subdivision surface approximates the input mesh. In each geometric iteration, we randomly select part of points on the subdivision surface to calculate the difference vectors and distribute the vectors to the control points. Finally, the control points are updated by adding the weighted average of these difference vectors. We prove the convergence of our method and verify it by demonstrating error curves in the experiment. In addition, compared with an existing geometric iterative method, our method has a faster fitting speed and higher fitting precision.